Multiple SVMs Modelling Method for Fault Diagnosis of Power Transformers

• Autorzy: Zhang M. , Li K. , Tian H.

Warianty tytułu

PL

Metoda wykorzystująca technikę SVM do diagnostyki transformatorów mocy

• Języki publikacji: EN

Abstrakty

EN

For enhancing the accuracy of fault diagnosis for power transformers, a multiple SVMs scheme is proposed in this paper. In this scheme, SVM is used to establish the base classifier for its good performance and fast learning speed. Secondly, the several base classifiers based on single SVM will be combined by consulting ensemble techniques. And then a multiple SVM s method is obtained. The real gas records data from a power company is used to establish fault diagnosis system for power transformers based on the new multiple SVM s method. For comparison, the conventional methods are used to build fault diagnosis models by the same data. The experiments demonstrate the new multiple SVMs method has the best performance in both learning ability aspect and generalization ability aspect for fault diagnosis of power transformers.

PL

Zaproponowano schemat SVM (support vector machine) w celu poprawy dokładności diagnostyki transformatorów mocy. W pprównaniu do metod konwencjonalnych proponowana metoda ma możliwość uczenia się i efektywnego wykorzystania bazy danych.

Słowa kluczowe

EN

fault diagnosis; SVM; power transformers; ensemble technique

PL

diagnostyka transformatora; SVM

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 7b
• Strony: 232--234
• Opis fizyczny: Bibliogr. 8 poz., tab.

Twórcy

autor

Zhang M.

• Politechnika Poznańska, Tianjin Polytechnic University

autor

Li K.

• Politechnika Poznańska, Tianjin Polytechnic University

autor

Tian H.

• Politechnika Poznańska, Tianjin Polytechnic University

Bibliografia

• [1] IEEE, “IEEE Guide for the Interpretation of Gases Generated in Oil Immersed Transformers, standard ed.” Picataway, NJ: IEEE Press, 1992.
• [2] “Interpretation of the Analysis of Gases in Transformers and Other Oil-Filled Electrical Equipment in Service:” IEC Publication 599, 1978.
• [3] Y. C. Huang, “Evolving Neural Nets for Fault Diagnosis of Power Transformers.” IEEE Transactions on Power Delivery, 18, No. 3, 843-848.
• [4] Y. C. Liang, X. Y. Sun, D. H. Liu, “Application of Combinatorial Probabilistic Neural Network in Fault Diagnosis of Power Transformer.” Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22(2007), 1115-1119.
• [5] Vapnik, V. N. (1999). The nature of statistical learning theory. New York: Springer.
• [6] Mayoraz, E., & Alpaydin, E. (1999). Support vector machines for multi-class classification. Proceedings of International Workshop on Artificial Neural Networks, 2, 833–842.
• [7] G. I. Webb, “NultiBoosting: A Technique for Combining Boosting and Wagging,” Machine Learning, 40(2000), No. 2, 159-196.
• [8] Y. Freund, R. Schapire, “Experiment with a new boosting algorithm,” Proc. Of the 13th Int. Conf. on Comput. Learning Theory, San Francisco, Morgan Kaufmann, 1996, 208-219.